Open AccessTOPIK MODELING PENELITIAN DOSEN JPTEI UNY PADA GOOGLE SCHOLAR MENGGUNAKAN LATENT DIRICHLET ALLOCATION
Author(s) -
Akhsin Nurlayli,
Moch. Ari Nasichuddin
Publication year - 2019
Publication title -
elinvo (electronics, informatics, and vocational education)
Language(s) - English
Resource type - Journals
eISSN - 2580-6424
pISSN - 2477-2399
DOI - 10.21831/elinvo.v4i2.28254
Subject(s) - latent dirichlet allocation , topic model , vocational education , computer science , hierarchical dirichlet process , process (computing) , artificial intelligence , probabilistic logic , informatics , generative model , generative grammar , mathematics education , engineering , psychology , pedagogy , electrical engineering , operating system
The mapping of research topics for lecturers is necessary to determine the research tendencies in a department or study program. This study aims to implement topic modeling in the publication titles of the Department of Electronics and Informatics Education Engineering of Universitas Negeri Yogyakarta (JPTEI UNY) lecturers taken from Google Scholar. The method used for topic modeling is the Latent Dirichlet Allocation (LDA). LDA is a generative probabilistic model for finding the semantic structure of a corpus collection based on the hierarchical bayesian analysis. After the topic modeling process, the results showed that JPTEI UNY lecturers tend to have four research clusters consisting of vocational education, system development, learning media, and vocational learning systems.